If a research question asks for cause and effect, what research method is appropriate?

If a research question asks for cause and effect, what research method is appropriate?

• A. Descriptive survey.
• B. Case study.
• C. Experimental design or controlled experiment.
• D. Meta-analysis.

Answer: (C)

To determine cause and effect, you need a study that actively manipulates a variable and controls other influences. An experimental design does this by assigning participants to different levels of the independent variable and comparing the outcomes. Random assignment helps ensure the groups are similar at the start, so differences in the dependent variable can be attributed to the manipulation rather than preexisting differences. A control condition provides a baseline, strengthening the claim of causality. By limiting or accounting for extraneous variables, experiments maximize internal validity and make the causal link clearer. Descriptive surveys map how things occur and reveal associations, but they don’t manipulate variables, so they can show correlation without proving causation. Case studies offer deep, contextual detail about a single case or a small number of cases but lack randomization and rigorous control, making causal inferences weaker. Meta-analyses combine results from multiple studies to estimate overall effects, but they don’t themselves test causality; their conclusions depend on the designs and quality of the studies included. So, for establishing cause and effect, an experimental design or controlled experiment is the appropriate method.

To determine cause and effect, you need a study that actively manipulates a variable and controls other influences. An experimental design does this by assigning participants to different levels of the independent variable and comparing the outcomes. Random assignment helps ensure the groups are similar at the start, so differences in the dependent variable can be attributed to the manipulation rather than preexisting differences. A control condition provides a baseline, strengthening the claim of causality. By limiting or accounting for extraneous variables, experiments maximize internal validity and make the causal link clearer.

Descriptive surveys map how things occur and reveal associations, but they don’t manipulate variables, so they can show correlation without proving causation. Case studies offer deep, contextual detail about a single case or a small number of cases but lack randomization and rigorous control, making causal inferences weaker. Meta-analyses combine results from multiple studies to estimate overall effects, but they don’t themselves test causality; their conclusions depend on the designs and quality of the studies included.

So, for establishing cause and effect, an experimental design or controlled experiment is the appropriate method.